timeseries_forecasting.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "colab": {
      "name": "Timeseries_forecasting.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v6_-KCwCmi2m"
      },
      "source": [
        "# Data Preparation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bSOSulFRmi2n"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M_oujiizmi2o"
      },
      "source": [
        "x = np.arange(3.1, 20, 0.2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a1H4k4eCmi2p"
      },
      "source": [
        "# Наша функция\n",
        "y = (np.sin(2*x) * x) + np.random.rand(x.size) * 0.2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KY73D9Zbmi2p",
        "outputId": "9c09b815-897b-411f-858c-e6201b26b636"
      },
      "source": [
        "plt.plot(x, y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x234f8991cd0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YsLiRjMFmi2r",
        "outputId": "ba960107-2d33-4b68-a5cb-96ba1e9c542a"
      },
      "source": [
        "len(x)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "85"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AQbpQukBmi2r",
        "outputId": "bdd860a4-dd7d-4c20-9c45-eac6b5fe5b50"
      },
      "source": [
        "raw_dataset = pd.DataFrame.from_dict(dict(zip(x.tolist(), y.tolist())), orient='index').reset_index()\n",
        "raw_dataset.columns = ['x', 'y']\n",
        "raw_dataset = raw_dataset[[\"y\", \"x\"]]\n",
        "raw_dataset.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>y</th>\n",
              "      <th>x</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.165349</td>\n",
              "      <td>3.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.217487</td>\n",
              "      <td>3.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.404158</td>\n",
              "      <td>3.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.345047</td>\n",
              "      <td>3.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.996026</td>\n",
              "      <td>3.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          y    x\n",
              "0 -0.165349  3.1\n",
              "1  1.217487  3.3\n",
              "2  2.404158  3.5\n",
              "3  3.345047  3.7\n",
              "4  3.996026  3.9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4xH7tw-Gmi2s"
      },
      "source": [
        "def create_shifted_df(df,shift):\n",
        "    shifted_df = pd.concat([df, df.shift(shift)[['y']]], axis=1)\n",
        "    shifted_df.columns = [\"y\", \"x\", \"y_\"+str(shift)]\n",
        "    shifted_df = shifted_df[[\"y_\"+str(shift)]]\n",
        "    return shifted_df.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYCDauJymi2t"
      },
      "source": [
        "test_df = raw_dataset\n",
        "for i in range(1, 6):\n",
        "    test_df = pd.concat([test_df, create_shifted_df(raw_dataset, i)], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "CWiKz9mLmi2u",
        "outputId": "584a5d9e-5033-49aa-ee7b-74d0f28fd8df"
      },
      "source": [
        "test_df = test_df.dropna()\n",
        "test_df.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>y</th>\n",
              "      <th>x</th>\n",
              "      <th>y_1</th>\n",
              "      <th>y_2</th>\n",
              "      <th>y_3</th>\n",
              "      <th>y_4</th>\n",
              "      <th>y_5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4.047101</td>\n",
              "      <td>4.1</td>\n",
              "      <td>3.996026</td>\n",
              "      <td>3.345047</td>\n",
              "      <td>2.404158</td>\n",
              "      <td>1.217487</td>\n",
              "      <td>-0.165349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3.241412</td>\n",
              "      <td>4.3</td>\n",
              "      <td>4.047101</td>\n",
              "      <td>3.996026</td>\n",
              "      <td>3.345047</td>\n",
              "      <td>2.404158</td>\n",
              "      <td>1.217487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.999863</td>\n",
              "      <td>4.5</td>\n",
              "      <td>3.241412</td>\n",
              "      <td>4.047101</td>\n",
              "      <td>3.996026</td>\n",
              "      <td>3.345047</td>\n",
              "      <td>2.404158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.268352</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.999863</td>\n",
              "      <td>3.241412</td>\n",
              "      <td>4.047101</td>\n",
              "      <td>3.996026</td>\n",
              "      <td>3.345047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>-1.643237</td>\n",
              "      <td>4.9</td>\n",
              "      <td>0.268352</td>\n",
              "      <td>1.999863</td>\n",
              "      <td>3.241412</td>\n",
              "      <td>4.047101</td>\n",
              "      <td>3.996026</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          y    x       y_1       y_2       y_3       y_4       y_5\n",
              "5  4.047101  4.1  3.996026  3.345047  2.404158  1.217487 -0.165349\n",
              "6  3.241412  4.3  4.047101  3.996026  3.345047  2.404158  1.217487\n",
              "7  1.999863  4.5  3.241412  4.047101  3.996026  3.345047  2.404158\n",
              "8  0.268352  4.7  1.999863  3.241412  4.047101  3.996026  3.345047\n",
              "9 -1.643237  4.9  0.268352  1.999863  3.241412  4.047101  3.996026"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "swQZA5J7mi2v",
        "outputId": "c75249a9-2b74-4eb6-e598-acea622d21e2"
      },
      "source": [
        "test_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(80, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QoaLPY91mi2w",
        "outputId": "9c040156-6023-4a54-9dc9-882189325050"
      },
      "source": [
        "test_df.tail(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>y</th>\n",
              "      <th>x</th>\n",
              "      <th>y_1</th>\n",
              "      <th>y_2</th>\n",
              "      <th>y_3</th>\n",
              "      <th>y_4</th>\n",
              "      <th>y_5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>9.305892</td>\n",
              "      <td>19.1</td>\n",
              "      <td>2.052720</td>\n",
              "      <td>-5.453945</td>\n",
              "      <td>-11.832672</td>\n",
              "      <td>-16.220601</td>\n",
              "      <td>-17.938274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>15.304517</td>\n",
              "      <td>19.3</td>\n",
              "      <td>9.305892</td>\n",
              "      <td>2.052720</td>\n",
              "      <td>-5.453945</td>\n",
              "      <td>-11.832672</td>\n",
              "      <td>-16.220601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>18.910674</td>\n",
              "      <td>19.5</td>\n",
              "      <td>15.304517</td>\n",
              "      <td>9.305892</td>\n",
              "      <td>2.052720</td>\n",
              "      <td>-5.453945</td>\n",
              "      <td>-11.832672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>19.614028</td>\n",
              "      <td>19.7</td>\n",
              "      <td>18.910674</td>\n",
              "      <td>15.304517</td>\n",
              "      <td>9.305892</td>\n",
              "      <td>2.052720</td>\n",
              "      <td>-5.453945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>17.171458</td>\n",
              "      <td>19.9</td>\n",
              "      <td>19.614028</td>\n",
              "      <td>18.910674</td>\n",
              "      <td>15.304517</td>\n",
              "      <td>9.305892</td>\n",
              "      <td>2.052720</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            y     x        y_1        y_2        y_3        y_4        y_5\n",
              "80   9.305892  19.1   2.052720  -5.453945 -11.832672 -16.220601 -17.938274\n",
              "81  15.304517  19.3   9.305892   2.052720  -5.453945 -11.832672 -16.220601\n",
              "82  18.910674  19.5  15.304517   9.305892   2.052720  -5.453945 -11.832672\n",
              "83  19.614028  19.7  18.910674  15.304517   9.305892   2.052720  -5.453945\n",
              "84  17.171458  19.9  19.614028  18.910674  15.304517   9.305892   2.052720"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5W2NaC6Wmi2w",
        "outputId": "7951ed6a-afa4-458e-942f-50d398be80f8"
      },
      "source": [
        "y = test_df.y\n",
        "y.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5    4.047101\n",
              "6    3.241412\n",
              "7    1.999863\n",
              "Name: y, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZMKJQNHDmi2w",
        "outputId": "1f828507-74ee-45a5-a6ab-89c8321c069d"
      },
      "source": [
        "X = test_df.drop(['y'], axis=1)\n",
        "X.columns = [\"x\",\"y1\",\"y2\",\"y3\",\"y4\",\"y5\"]\n",
        "X.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>x</th>\n",
              "      <th>y1</th>\n",
              "      <th>y2</th>\n",
              "      <th>y3</th>\n",
              "      <th>y4</th>\n",
              "      <th>y5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4.1</td>\n",
              "      <td>3.996026</td>\n",
              "      <td>3.345047</td>\n",
              "      <td>2.404158</td>\n",
              "      <td>1.217487</td>\n",
              "      <td>-0.165349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4.3</td>\n",
              "      <td>4.047101</td>\n",
              "      <td>3.996026</td>\n",
              "      <td>3.345047</td>\n",
              "      <td>2.404158</td>\n",
              "      <td>1.217487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>4.5</td>\n",
              "      <td>3.241412</td>\n",
              "      <td>4.047101</td>\n",
              "      <td>3.996026</td>\n",
              "      <td>3.345047</td>\n",
              "      <td>2.404158</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     x        y1        y2        y3        y4        y5\n",
              "5  4.1  3.996026  3.345047  2.404158  1.217487 -0.165349\n",
              "6  4.3  4.047101  3.996026  3.345047  2.404158  1.217487\n",
              "7  4.5  3.241412  4.047101  3.996026  3.345047  2.404158"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ODDbkYgXmi2x"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, shuffle=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3rSwZh_8mi2x",
        "outputId": "9d0a0abd-598e-436c-c7fb-a3aa67f693f6"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(53, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KruAhNG-mi2y",
        "outputId": "4e3354ba-a899-4327-e948-7e1b72013fc2"
      },
      "source": [
        "plt.plot(X_train.x, y_train)\n",
        "plt.plot(X_test.x, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x234f8b79880>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JPtYUZbpf5PvOQ0oOpFnUl9D8RODGxVelINilRV6jiUYorHjtfB97Vy1suv31awsYmQhx3ENdGRu7g6wtnv8b2VxsKs12v8ib9kmrBlqn5RkLm27M5McGYGpMl2s0mmic7hhkaHyKa1YvbKjENasLEDE+BXiB4PgUnYNjC/q0MhebSrPoGhynb3jCgshsor/Juno8GG8Wbm05bHrkdSav0VzKq+cMkb561cJEPjc9mS1l2bzU6I26/NnuIIBlmTzAGbdm8+GwIcZWijy4t+Ww6ZF3WR95Ey3yHkEpxcOvtXD/92s52eZSG9kiOHi+j8r89MtugJqL69YUcrR5gNGJkA2RWctZvyHy1mTypsPGpSI/1AGhCes2QpnkVhpvHspl+yN0Jq+JlfM9w7z/Wwf580frOFDv587/8yJf/OUpguNTTocWE+Gw4rWmPvYuMIs3uW5NAROhMLUX3L+ZurE7SKJPqCpIj/lahZkpFGWlcNqtNsp+i7pPziavCiZHYNhln960yGsWSzis+PqBRt72z89zsj3Al+7Zxmt/dSvv31vJgy83cevXnuOZM11Oh7lo3ugeYmBkkr2rF7boarKnOp9En/BSo/vr8mf9QaoK0mNy1szE1YuvVnvkTXJd2nJ4qNNwEiVnOB1JVLTIu5jHjrbxlSfr2behmKf/5028f28lOelJ/N3d2/jpH11HbnoSn/jBEXqC406HuigOnjMy8MVm8hkpiVxVmcsrHvDLn/UPW1KqMdlUmkVjd9CdFtL+JpAE69vu5rm05bCLPfKgRd61KKX41gvn2FCSxTc+uIvi7DdPQ7qqMo//8/5dTITCPHTQpbsAr8DB872U5aRSnrfwerzJtWsKOdEWIDDq3kEak6EwTT3Dliy6mqwrzmIiFKalb8Sya1pGf5PRcjchydrrujmT1yKvWSgvNfZypnOIj96wCpnDa7y2OJMb1xfxg1cvuDOjuwxKKQ6d72Pv6oI5/33z4fo1BYTVRZeOG2nuG2EqrCzN5M3a/gW3irzVpRqAlExIL3Cfw0aLvGYxfPvFcxRmpnDXzrLLnveR66vpHhrn1yc74xSZNZz1D9MTnFh0qcZkZ2UuSQni6k1RjRbaJ02q8g2Rd20mb0Uf+WjkVrmrf41Sxtg/LfKahdDQNcSBej/3XVtFSmLCZc+9aV0RqwozePCl83GKzhoOnjcy78UuupqkJCawtjjLvXZCLtonVxdZtzBXlJVCWlICF3pdJvKTYzDsh5zLt4xeNNllF33pbmC037CLunS3K2iRdyXffek8KYk+PnDNlbMhn0+479oqjjQPuDqbnc3Bc30UZaVQbYGlcHNpNq+3u1fkG7uDrMhOJSvVuhq1iFCZn+4+kR9sM445Nk1Iyl4Jg+32XHsxTA8LWcaZvIg0icgJETkmIrV238/r9ATHefRIG+/aXU5+xvxmY75rdzmZKYl87+Ume4OzCKUUB8/3sndVfkz1eJPNZdl0D43jH3Kny+isf5g1xdbb6yoL0mnuc1lfebtnnWaXwfggjLnkTX1a5HUm/1al1E6lVE2c7udZfvjqBSamwnz0LfPfLZiVmsQf7C7nl3XtdA+N2RidNVzoHaFrcDzmUo2JmwdcK6U42x1krYWLriZV+ek0942g3LQD1HaRj3xCcEvJxuUboUCXa1zF2GSIH7xygVs2Fi/YiXHfddVMhhQPHWyxKTrrqL3QD8DV1bEtuppsdvE2/+6hcYLjU6yxcNHVpLIgnbHJMN1u+gRjlmvsGmidXfbm+ziNy/vWQHxEXgFPichhEbl/9pMicr+I1IpIrd/vj0M47uVAvZ/e4Qn+8LrqBf/uqsIMblxfxCOHW9yV2UXheMsAGckJlrlNctKTWJmb5sq6/HRjMhsy+cqIw8ZVdflAC2QUQ2KKPdefFnmX1OWHuiA1F5JSr3iqU8RD5K9XSu0Cbgc+JSI3znxSKfWAUqpGKVVTVFQUh3Dcyy/r2inISOb6NYsrY9y2uYTW/lHO+l1Wp51FXesAW1fmkOCzqNc4xjZ/N2byjWZjMhsy+aoCo87f7CYbZaDVvlINXKx9u0bkO1xdj4c4iLxSqj1y7AYeA662+55eZHh8iqdPd3H7thUkLrK/yc3rjTfJA/XdVoZmKRNTYU53DLGjItfS624uy+acP+i6jpRnu4NkpSRSnGV9ZrsyNw2fQLObhnoH2uxz1oCRMacXuqhc426PPNgs8iKSISJZ5vfA7wEn7bynV/nd6S7GJsO8c/vlNz9djor8dNYWZ3Kg3r1lrzOdg0yEwmwvX/ww62hsLs0mrKC+y12dGRv9QVYXZ1riIppNcqKPstw09+x6VSqSyVvcs2Y22WUw6KKF12WeyZcAL4rIceAQ8Cul1JNW3yQwOsmfPnycXo826gJ4vK6DkuwU9sS4GPnWDUUcOt/HsEvbEB9vNXrh7yjPtfS6W8oii68uq8uf7R5mjYWboGZTVeAir/xoP0wO21uugYjIu6BcEw67frcr2CzySqlzSqkdka8tSqm/t+M+TT3D/OpEOx958DVP9lgPjE7yXL2fd2wrwxdjnfqtG4qZCIVdOxavrmWA/IzkmJqSRaM8L42slERe73DPQJWhsUk6B8csbWcwm8r8DPfU5O121phkl7mjXDPSC+Gp5S3y8WJHRS7/9v5dnGof5I9+eJiJKW8163rqVCcToTDv3BH7x76a6nwykhNcW5evaw2wvTzH8vKFiLCpzF07X89FFsCtbEw2m8r8dPqGJxgac0EXzmmPfBzKNaN9MDlq732uRND9HnlYIiIPcMumEr78+9t4oaGHzz1ynHDY3TbCmfyyroPyvDR2WrAYmZzo4/q1hRyo97vOSjkyMUVD9xDbLS7VmGwuzeZM5xAhl/y/N3vW2JnJT3ejdEPJxu6NUCbmJwWnSzbTG6GWd00+rtxbU8Ff7N/IL4638/88/rrrRC4afcMTvNTYwzt3lFmW3d68oZi2gdHp7odu4WTbIGEFOyxedDXZXJbNyESICy5xm5gj/0w/ux2Y13ZFySbQCr4kyLDZCu0Wr7wH+tbAEhN5gE/ctJr/dv0qHny5id++7v7ReL8+2UEorLhju3XZwM0bjBfZsy4r2dS1DgDYmsmDe3a+nu8ZpjLfupF/0TAzedeIfM5K8NksK27L5DNLnI3jCiw5kRcR/vLtG1lXnMnf/up1xibd5ZuezS+Pt7O6KGNaoKygLDeNDSVZrrNSHm8NUJaTSpENnnGAdSWZJPrENXX58z3DrCq0d+5nVmoS+RnJ7inXZNtcqoEZG6IcXnwd6jCGmNi1u9cilpzIAyQl+PjinVto6RvlgefPOR3OnPQPT3DwfB93bCu1fCHy5o1FvNbU5yq3UV3rgG1ZPJi95TNdkcmHw4oLvSNU2yzyYJRsXNGNcrDN/no8GBOiUnNckMl3ubpnjcmSFHmA69YW8vZtK/j6gUZa+12Q5UTh+QY/SsFbNxZbfu2b1xczGVK81OiOIdcDIxNc6B1he4U99XiTzS5x2HQNjTE6GYqLyLvCKx+aMkQ3HiIPRsnG6U6ULh/gbbJkRR7gr96xGYAvPXHa4Uii89wbfvLSk2zJbmuq88hMSXRNyabOpk1Qs9lc6o7e8ud7jMx6dZwy+faBUWetw8FOUCF7WxrMJKvUBeUa9+92hSUu8itz0/jkzWt54kSnazJak3BY8fwbPdywrsjSRl0mSQk+9lTn8VpTn+XXXgzmouvWlfZn8mC0T3CSph4js45XuSasoG3AQd94wJwIZbNH3sTpXa/hEAS7dCbvBu6/cTUV+Wl88ZenXOWdf71jkJ7gODett89uVlOdT2N3kL7hCdvuMV+OtwZYXZhBTpp1I/Cisb4kC4CGLmfto+d7gqQk+ijNtr8FrdmN0lHraCAyxyCe5ZpgN0w59Lc93GN8ctEi7zypSQl87vc28EZXkOca3FG6AKNUA3CjjSJv9sE5HBnS4SR1rQOWd56MRkFGMnnpSTQ4vEfgfM8I1QUZMbepmA+usFGaG6Hsbmlgkl0GqIu7TuONR3a7wjIQeYDbt5ZSnJXiqhmoB+q72boy2zY7IcD28hySE3zUXnC2ZNM1OEbX4LjlnSejISKsK86isdvZbpRNvcNUF9q3CWomxVkppCb5aHZy8XWwDVJyINU6K/BlcdorPxTZg6PdNe4gOdHH+/dWcqDeT1OP81azwOgkR5oHbC3VgPEpZuvKbGqbnM3kT0QWXbfZXI83WVuSSUN30LEdz6GwojlO9kkw3tgq89OdbTls97CQ2Tg9BtDM5DOtd8ZZzbIQeYD3760kKUH4wasXnA6Flxt7CIUVN2+w/w9kT3U+da0Djm4KO9U+iIgxvSkerC3KZGBkkl6H1iLaB0aZCIXj4qwxqcxPdzaTD7TEz1kDzrc2mM7k3b3bFZaRyBdnpXL71lIerm1hZMLZDUIH6v1kpSZyVRxq1DXV+UyG1LSF0QlOtQdYVZhBRkpiXO63rsRoCObU4qtpn6wuiKfIGy2HHevXFIjTRiiT1BxIynBueEiw0/WzXU2WjcgD3HddFUNjUzx21Dl/rVKK597wc8O6wkWP+VsIu6vyAByty59qH2RLWXxKNQDrig2HjVN1eVPk7W5pMJOK/DRGJ0POfHqZGDZa/8ZT5EWc7SvvgbF/JstK5HdV5rGlLJvvv3zBsYynvmuIzsEx2+vxJvkZyawpynCsLt8/PEHbwChby+K0IAeUZKeQmZLomMPmfM8wGckJti6qz6Yiz1jkbXGiLm965OPRt2Ym2aXOlWuCXZ4o1UAcRF5E9otIvYg0isjn7b7fFWLhvmurqe8a4uB5ZzLb5+rtt07OZk91PrVNfY7sEzD7yMQzkxcR1hZnOtZq2XDWZNgy13UuKiIth1v6HdgQNRinPvKzyV7pbE1eZ/IgIgnAvwG3A5uB94nIZjvveSXu3FlGbnqSY3bKA/V+Nq7IojTH2vF3l6OmOp/BsSlHMttT7cZawJY4ZvIA64ozHc3k41mqAabHKTqTyZsiH8eFVzDKNUMdxu7TeKIi/nydyQNwNdAYmfU6AfwYuMvme16W1KQE7t1dzm9f76InzoO/B0YmONTUZ0tDsstR42Bd/mTbIGU5qeRlJMf1vutKMvEPjTMwEt8a9WQoTGv/aNxFPiMlkfyMZGea8QVaAYGssvjeN7vM2HUajPPchNF+CE3oTD7CSqBlxs+tkcemEZH7RaRWRGr9/vjsSH13TQVTYcVjR+K7aPO7092Eworbt8b3j6OqIJ3CzBRH6vKn2gNsiZM/fibmyL14l2xa+kYIhVVcnTUmFXlptDpRrgm0GYKXGN83csc2RAW9Y58E+0U+WlHyTYVhpdQDSqkapVRNUVF86tTrSrK4qjKXh2tb4roA++TJTspyUuO2KchERBxpVjYyMcW5nuG4l2rgosMm3iWbaWdNUfxFvjw/3aFyTUv82hnMxKkNUUPeaWkA9ot8KzCzLV054HCnf4N311TQ0B3kWMtAXO43PD7F8w1+3rZ1RVwX5ExqqvNp7R+lMzAWt3ue7hhEqfguupqszE0jNckX90x+WuQdyeTTaRsYjf8g80AL5Map++RMzDeWePeVD3qnpQHYL/KvAetEZJWIJAPvBX5h8z3nxR3bS0lN8vFwbWtc7neg3s/EVJj9W5z5w3CiLn+q3XTWxD+T9/kMh028M/mm3mFy0pLivgYBhld+MqToGozfGznhMAy0QG5l/O5pkl4ACckXF37jxXQmr8s1KKWmgE8DvwFOAw8rpU7Zec/5kpWaxNu3lfLL4+2MTti/Ov/kqU4KMpKpiXSGjDeby7JJS0rgtThaR0+1DZKfkUxpjjO7AtcWZdLYFd8NUU44a0wc8coHOyE8Gb8+8jMxN0Q5kcknZUBKVnzvu0hs98krpZ5QSq1XSq1RSv293fdbCO+uqSA4PsWvT9r7RzI2GeKZ01383pYSWwaEzIekBB+7qnJ5LY6LryfbA2wpy3akPAXG2kt7YCyuc26bekacE3knvPIDEV9FblX87jmTLAeGhwx1eiaLh2W243U2e1flU1WQzsO1LVc+OQZePtvD8ESItzlUqjHZU53P6c5BBscmbb/XxFSYN7qGpic1OYHpsDkbp5LN2GSI9sCoI84agLLcVESIr41yoNk4OlGuAWdaGwS9McDbZFmLvIhw7+5yXj3XZ+tUnSdPdpKVksh1awptu8d82FOdj1LxGSLS0D3EZEg5suhqsi4i8vGqy1/oHUEpZ5w1ACmJCZRkpdLSF8dMPmCKvAPlGrg4BjCebUp0Ju8t3rW7HJ/AIzYtwE6Fwvz29S5u2VRMcqKz/7mvqswl0SdxqcufajMWXePZs2Y2lfnpJCf4aIhTozInnTUmFflptMQ7k08vgGSH/s3ZK42NSSO98bunzuS9RWlOGjetL+Lh2hYmQ9ZPuz/U1Ef/yCT747wBKhrpyYlsWZkTF7/8qfYAGckJjpUuABITfKwqzKAxTi2Hz/qN+8RrIlQ0KvLSaY3nwutAizOLribx9sqPB2EiqDN5r/Gha6voHhrnqVNdll/7yZOdpCb54tqQ7HJcXZ3H8ZaA7UNETrUPsqk0Oy4zTi/H2pJMGv3xEfnG7iBlOalkpdo7rPxylOen0zE4xsSU9QlLVAaanavHw4xdr3Fy2HjMIw9a5AG4aX0x5XlpfP+VJkuvOzEV5vG6DvZtLCY9OT4DM67Enup8JkJhTrTZN0QkHFac7hhkqwPtDGazrjiT5r6RuEzGaugeYm2Js7a6irw0lDKmU9mOUpGNUE6KfJwzeY955EGLPAAJPuGD11Rx8Hwf9Z3W1W8P1HfTNzzBu3bFuQXrZTB9+odsrMu/0T3E8EQo7u0borGuOAul7O9hEw4rGruD04u9TmHaKOPSw2bYD1Njzop8ZjFIQvxslNOzXXUm7zneXVNBcqKPH7zaZNk1Hz3SSmFmsmtKNWAMEVlbnGlrXd5079RU59l2j/myYYUhumcsfPOORtvAKGOTYdeIfFwWX02PvJM1eV8CZMVxeIg529UjfWtAi/w0+RnJ3LG9lMeOtDFkgY+8f3iCZ850c9fOlSTFYczfQthTnc/hpn7bepwcbuqnMDOZynznFiBNqgsySE70Ud85aOt9zE8K5nxZp1iRnUqiT+Kz63XggnF0MpOH+Hrlg51GK4U05xOY+eIu9XGYP7y2muGJkCUzYH9xvJ3JkHJVqcbk6lV5DI1PccYm4Tvc3M/uqjzHdrrOJDHBx7riTNszedOmubbI2Zp8gk8oy02Lz67XAYc98ibZcdz1OhQZ++eCv+35okV+BjsrctlensP3X4l9BuyjR1rZVJrt6I7PudgTqcvb4Zf3D41zoXdkeoC4G9iwIsvStZZoNHQFKcpKISfdOWeNSUV+Wnwy+UALpOYYX04Szw1RHpoIZaJFfhYfvKaKxu4gr55bvAA2dA1R1xrgXbsc6LE9D1bmplGak8prNux8Nevxu6ucacQWjY0rsugeGqd/2L4pUQ0uWHQ1qchLj09rA6ftkybZZTA5DGP2Ocam8dBsVxMt8rO4c0cZeelJfP1A46Kv8ZMjrST6hLuvcqfIG0NE8nntfJ/lQ1MOX+gjOcHH1pXu+QSzYYURi10lG6Xc4awxqchPpyc4YX931YEWyHGJyEN8SjY6k/c+qUkJfPLmtbzQ0MPLjT0L/v2pUJjHjrRx84YiCjNTbIjQGvasyqd7aJxmiz/WH77Qz7byHFISEyy9bixsXGHUye1afO0cNDpdOu2RNzGHetuazSvlokw+TmMAp8aN+a46k/c+H7q2irKcVL785JkFZ7ovNvbQPTTuygXXmexdZZRTXjlrXc+PsckQJ9sGpweUuIXirBRy05Oot6m3fEOkbYKbMnmw2UY52m+USJxedIWLmfyQzSLvsdmuJlrko5CalMBnb1tPXWuAJ050zvv3lFJ848BZCjKS2bep2MYIY2ddcSalOak8W2/dpPuTbQEmQmF2uUzkRYQNJVm2lWvMLpduEXkzk7e1G6Vb7JMQ2Zgk9mfyHvTIgxb5Ofn9XeWsL8nkq0/Vz7tx2a9PdnLwfB+fvW29q8oV0RAR3rqxmBcbehifsqZ2Wzu96OoukQejZFPfOUTYhr0Bjd1D5GckU+CS8lxRZgqpST57HTZu2Ahlkphs7Hy12ys/vdtVZ/JLggSf8Bf7N3K+Z3heQ0XGJkN86YnTbFyRxfuudkF2Mw/2bShmeCLEa+etcdkcvtBPdUG6K9ciNpZmMzIRsmW7f0NXcHpAiRsQEcrz0u0t1zg9LGQ28fDKT/et0Zk8ACLyNyLSJiLHIl9vt+tedrFvYzF7qvP45981MDJx+RFy337hHK39o/z1Ozc7NuJvoVy3toDkRB9Pn4m9+6ZSiiMX+l1lnZzJhsjiq9UbwJRSrrJPmlTmp9NsZ7km0ALJme7Z+Zm90n6RD3aB+CDDPW1K5oPdmfw/KaV2Rr6esPleliMifP72jfiHxvnMj47OKfSdgTH+7dmz7N+ywvHpTwshPTmRa1cX8OyZ2OvyTb0j9A5PuLJUA7C+xHTYWFuX9wfHCYxOuk7kqwsyuNA7bLlFdhrTWeOWnZ/xaG0w1GkIvM/dpdjZ6HLNFdhdlc/f3r2VZ+u7ec+/v0r34Ngl53zlyTOEwor/6+2bHIgwNvZtLKapd4RzMfZcd1NTsmhkpiRSkZ/GGYsdNuZAknUusU+arCpMZ2QiRPfQuD03cIt90iSr1NgMNW5jt9Fgl+fq8WC/yH9aROpE5LsiEvXVLyL3i0itiNT6/X6bw1kcH7qmim/9YQ2N3UHu+frLvNE1RGdgjMfr2vnCz0/y06NtfOyGVVQWON+Qa6Hs22i4gJ6JMZs/fKGPrNRE1ha5K6OdyYaSbMszebc5a0yqC42JXOf8Ns0udnoi1GxMr/yQjcNDgt7b7QoxiryI/E5ETkb5ugv4BrAG2Al0AF+Ldg2l1ANKqRqlVE1RkXtrXbdsKuHhj1/LRCjM7f/yAtf8w9N8+kdH+a/aFm7dVMIn37rW6RAXRUV+OuuKM2O2Ur7W1M+uyjzHJ0Fdjo0rsjjfM2yZmwiMxmTZqYkUZblrsdkcu9hkx4D60QEYD7grk4/H8JAhb2byMY0rUkrdOp/zRORbwOOx3MsNbCvP4Wefup5vv3COlblp1FTns6Us23WthBfKvo3FfPel8wTHp8hMWfifxDl/kMbuoOtdRRtWZBGKDPfYUmZNU62GriDrSrJc0XFzJmW5aSQn+mjqsUHkAxG3mRs2QplMi7xNmXxoCoa7jbKQx7DTXTPzv8Y9wEm77hVPVuam8YV3buFjN6xmZ0Wu5wUe4K0bi5kMKV5sWFy57IkTxgvr7dvc/VH2YnsD60o2jd1BV5aoEnxCVX465+0QebfZJ8H+TH6oA1QYctzZj+py2KlQXxGREyJSB7wV+KyN99LEwO6qPLJSE3n69OJKNo/XdbC7Ko/SnDSLI7OW6sIMkhN8lu187Q2O0zs84figkLmoLsywSeTNjVAuEvmkNEjLt89GGWg1jtnublcSDdumSyulPmTXtTXWkpTg48b1RTxb7yccVguqq5/zBznTOcRf37HZxgitISnBx1oLB4i8EXHWuGkj1ExWF2bw3BsL/396RQaaITENMlxmF7bTK29+Qsjxnsh7v9agsYRbNhbTExznaMvCdr+apZrbXV6qMTHaG1izIepE2wAAW10wsDwa1YUZTEyFaQ9YvCmq/zzkVbvHI29ip1fezOR1uUbjVX5vywoykhN46NCVWzjM5PG6Dmo8UKox2VyWTdfgeNT9DgvleGuAlblprmzjABcdNpaXbHoaoNCFbjI7WxsEWo0JWCnu2g8xH7TIawBjs9CdO1fyeF07gdH5DTI/GynVvH2bdxwHZofMI82x9+s53jLAzorcmK9jF6siXnlLHTahKSOTL3CjyK+EkR6YjP0N/BIG2zxZjwct8poZfGBvJWOTYR470jqv85+oM1013hH5LWXZJCf4pnfoLpbe4Dit/aNsL3dnqQagJDuFtKQEzvdY2Khs4AKEp1wq8mZfeRtslIFWT5ZqQIu8ZgZbV+awvTyHHx1qnlfPk1+dMEo1K3JS4xCdNaQkJrCtPIcjzQMxXaeu1Zgnur08N/agbEJEIg4bC7f690bGYhass+6aVpEdSTbsKNkEWj256Apa5DWzeP/VlbzRFbxipmuWat6x3TtZvMnuqjxOtAZi2vl6vHUAEWODnJtZVZhOU6+Fmfy0yLsxk7eptcHECIz2Xby+x9Air3kT79xRRmZKIj862HzZ88xSze1bvSfyuypzmQiFOdW+eJfN8ZYB1hZlLmqHcDxZVZhBS98IU/McfHNFehuN9sIZBdZcz0pMER64/N/ugvGwfRK0yGtmkZGSyN1XlfH4iQ4GRiaintM1OMaDLzexd1W+p0o1JrsqI4uvi6zLK6Woaw2ww8WLribVBRlMhZV1w1J6GtyZxQOkZEJ6gfUiP22f1CKvWSK8/+oqJqbCPHrkUs/xVCjMZ350lNHJEH9/z1YHooud4uxUKvLTFr342jYwSu/wBDtcXqqBiw4by2yUvWfdK/JgtFow589ahZnJ63KNZqmwuSybnRW5/OerFy7J5v/xt29wqKmPL92zjbXF3vMMm+yuzONIc/+ihmocbzEWXT2RyVsp8uNBGGp3uchXQb/FIh9oBeSie8djaJHXROWPbl5DU+8w+772HA+/1kI4rHj2TDdfP3CW911dyd1XeTOrMdlVlUfX4DhtAwsvY9S1DpCc4GPjimwbIrOWgoxkslITrWk53Hc2clEXi3xeldElM2zRGgQYIp9ZDInu3PR2JbTIa6Lyti0rePwzN7C6MIM/f7SOd33zZT778DE2lWbzhXe6v0/NlZiuyy/CSnmsZYBNZdkkJ7r/5SMirLKqUZmbnTUmuVUQmrDWYTPY5tlSDWiR11yGzWXZPPzxa/nqvTsiDg3F1z+wi9Qkb824jMbGFVmkJycsePE1FFacbAt4oh5vUl1glcibmfya2K9lF7lVxtHKxVcPe+TBxi6UmqWBzyf8we5y9m9dwfD4FCXZ3nPTRCMxwceO8twFL76e9QcZngi5ehPUbFYVZvDLunbGp0KkJMbwBt3TYIz8S3Jxn6I8U+QvQNW1sV9PKQi0wdp5zUdyJTqT18yLzJTEJSPwJrur8ni9Y5CRial5/87xlgEAdlZ4J5NfVZiBUtAc66ao3kZ3Z/Fwce6sVYuvYwMwOazLNRqNF9ldlUcorKZbFMyH460DZKYksrrQnT3ko2GJw0apiH3She0MZpKUaozos8pG6XGPPGiR1yxjrqrMBRbWkbKuNcC2lTmuHlg+m1VWDPUe9hvDu9286GpipY0y4O3drhCjyIvIvSJySkTCIlIz67m/FJFGEakXkbfFFqZGYz256cmsKcrgcNP8RH5sMsTpjkG2e6hUA5CTnkR+RnJs3ShNZ40b+8jPJrfSuoXXQXPs3/It15wEfh94fuaDIrIZeC+wBdgPfF1EvG/J0Cw5rl1TwMtnewmMXLmH/tOnu5kMKa5b47Kxd/PgYzes4vq1MfSb8YJ90iSvyhDn0PzmIlyWQCv4kiCzJPZrOURMIq+UOq2Uqo/y1F3Aj5VS40qp80AjcHUs99Jo7OB9V1cyOhnikcNXnoj149eaWZmbxlvWek/kP3nzWu7YHsOOzZ4GSEi5uLDpZnKrQIUv1tNjIdBmtDD2ebeybVfkK4GZr5rWyGOXICL3i0itiNT6/X6bwtFoorOlLIc91Xl8/5ULhMJztzho6RvhxcYe7q0pJ8FD9XjL6D0L+avB54EP5DNtlLESaPXGG9tluKLIi8jvRORklK+7LvdrUR6L+gpSSj2glKpRStUUFRXNN26NxjLuu66a5r4RDtR3z3nOI7VGznJvjbdf8IvGC/ZJk9xK42jF4utgq6fr8TCPzVBKqcXsAmgFZr4aygGbJuxqNLHxti0rKMlO4XuvXOCWTZfWXqdCYR6ubeWm9UWszHXxRiC7CE1B3znYcLvTkcyP7HKQhNgXX8MhGOzw7Ng/E7vKNb8A3isiKSKyClgHHLLpXhpNTCQl+PjA3iqef8PPWf+lo/Keb/DTOTjGe/cs0yw+0AzhSSh0uUfeJCHREOZYyzXBbuPf7WH7JMRuobxHRFqBa4FfichvAJRSp4CHgdeBJ4FPKaUWP2tNo7GZ915dQVKC8INXLhWGhw61UJiZHDXLXxb0eqD75Gys8MpP95FfxiKvlHpMKVWulEpRSpUopd4247m/V0qtUUptUEr9OvZQNRr7KM5K5R3bSvnJ4VaC4xfbHHQPjvHMmW7etbucpATvOixiwh8x0HlN5GPN5Kd3u+pyjUazJLjvumqC41N89Tf1HL7QT//wBI8cbiUUVrxnuS64AjS/Yohmhoeso3lVEOyCyRjGHi6Blgagu1BqNNPsrMjlhnWFPPhyEw++3DT9+N5V+awu8k6vGksJh+HCS7DhHU5HsjCmWw63QNH6xV1jsA2SMiA117KwnECLvEYTQUT4jw/voblvhKbeYc75h2npG+GeXd7O5GKi+3UY7YfqtzgdycKY6ZVfrMibfeTF2/sitMhrNDNITPCxuiiT1UWZ7NvodDQuoOlF41h9vbNxLJRpr3zT4q8RaPF8PR50TV6j0VyOpheM0ocpml4hc4XRhmGxi69KQU+jtxab50CLvEajiY5Zj6++welIFo7PB7kVi7dRDrbBxBAUbbA2LgfQIq/RaKLjP+3NerxJbtXid712nzGORZusi8chtMhrNJroeLUeb5IXg1feHxH5Yi3yGo1mqeLVerxJbqXxSWRscOG/6z8NGUWQnm99XHFGi7xGs9yYGIFD34KOurnPCYehyaP1eJPcGFoOd5+BoqVhr9Iir9EsN1QIfvc38Oo35j7HfxpG+7xbjwej/z0YA08WglJGK4clUKoBLfIazfIjJQu2vwdO/RRG+qKf4/V6PBgi7UuEzst8YonGEnLWgBZ5jWZ5suejMDUGx34U/fmmF4yatlfr8QCJKYbQdxxf2O8tIWcNaJHXaJYnJVug4hqo/a5Rf59JOAwXXvZ2Pd6kdIch8mru0Y6X4D9tHHW5RqPReJo9H4W+s3D+uTc/3nUSRnq9XY83Kd1p/FsWMtTbf2bJOGtAi7xGs3zZfBekF0Dtdy4+Nh6En38SkrNgzS3OxWYVpTuN40JKNkvIWQNa5DWa5UtiClz1QTjzBAy2GzNNf3o/dJ2Ce/8DspbAJKySLSC++Yv8EnPWQOzj/+4VkVMiEhaRmhmPV4vIqIgci3x9M/ZQNRqN5ez+CKgwHPm+Yaus/xXs/zKsu83pyKwhOR0KN8xf5KedNUsnk4+11fBJ4PeBf4/y3Fml1M4Yr6/RaOwkfxWsvQVe+heYHIE9H4Or73c6Kmsp3QHnnp3fudPOmqUj8rHOeD2tlKq3KhiNRuMANR81BH7NPtj//3p+SMYllO00RgEOdV753CXmrAF7a/KrROSoiDwnInN6sUTkfhGpFZFav99vYzgajSYqG26H9/0XvPv7kLAE5wiV7jCO8ynZdJ+BjOIl46yBeYi8iPxORE5G+brrMr/WAVQqpa4C/ifwIxHJjnaiUuoBpVSNUqqmqKhocf8KjUazeERgw35jJ+xSZMU24zgfkfefWTI7XU2u+LatlLp1oRdVSo0D45HvD4vIWWA9ULvgCDUajSYWUrKMCU9XEnnTWbPzffGJK07YUq4RkSIRSYh8vxpYB5yz414ajUZzRUp3Qvuxy58TaF1yzhqI3UJ5j4i0AtcCvxKR30SeuhGoE5HjwE+ATyil5uiEpNFoNDZTugMGW2G4Z+5z/BEPyRIT+ZhWWZRSjwGPRXn8UeDRWK6t0Wg0ljFz8XXtHDt5l6CzBvSOV41Gsxwo3W4cL1eX7zq15Jw1oEVeo9EsB9LyjElRc4n82CCc/qWxV2CJoUVeo9EsD8p2Qsex6M8dfwgmgrB3ie32RYu8RqNZLpTugP4mCHa/+fFwGA49ACtrYOVuR0KzEy3yGo1mebDxDmMc4FP/95sfP/cM9DbC3o87E5fNaJHXaDTLg6IN8JbPQt2PoeF3Fx8/+ICx4Lr5bsdCsxMt8hqNZvlw459B4Xp4/E9gfAh6z0LDU1DzEUhMdjo6W1iC3Yg0Go1mDhJT4M5/he/uh6f/FnwJxtfujzgdmW1okddoNMuLymuMvvmHHoCkNGMMYnap01HZhi7XaDSa5cetX4DslUYf/b2fcDoaW9GZvEajWX6kZBlzbBufhvI9TkdjK1rkNRrN8qTiauNriaPLNRqNRrOE0SKv0Wg0Sxgt8hqNRrOE0SKv0Wg0Sxgt8hqNRrOE0SKv0Wg0Sxgt8hqNRrOE0SKv0Wg0SxhRSjkdwzQi4gcuLOJXC4HLjGF3DLfGBe6Nza1xgXtjc2tc4N7Y3BoXLC62KqVUUbQnXCXyi0VEapVSNU7HMRu3xgXujc2tcYF7Y3NrXODe2NwaF1gfmy7XaDQazRJGi7xGo9EsYZaKyD/gdABz4Na4wL2xuTUucG9sbo0L3BubW+MCi2NbEjV5jUaj0URnqWTyGo1Go4mCFnmNRqNZwnhe5EUkQUSOisjjTscyExHJFZGfiMgZETktItc6HROAiHxWRE6JyEkReUhEUh2M5bsi0i0iJ2c8li8ivxWRhsgxzyVx/e/I/8s6EXlMRHLjHddcsc147nMiokSk0E2xichnRKQ+8nf3FTfEJSI7ReRVETkmIrUiEvfpISJSISLPRvThlIj8ceRxS18Dnhd54I+B004HEYV/AZ5USm0EduCCGEVkJfA/gBql1FYgAXivgyE9COyf9djngaeVUuuApyM/x5sHuTSu3wJblVLbgTeAv4x3UBEe5NLYEJEK4DagOd4BzeBBZsUmIm8F7gK2K6W2AF91Q1zAV4AvKqV2An8d+TneTAF/qpTaBFwDfEpENmPxa8DTIi8i5cA7gG87HctMRCQbuBH4DoBSakIpNeBoUBdJBNJEJBFIB9qdCkQp9TzQN+vhu4DvRb7/HnB3PGOC6HEppZ5SSk1FfnwVKI93XJE4ov03A/gn4M8Bx5wUc8T2R8CXlVLjkXO6XRKXArIj3+fgwOtAKdWhlDoS+X4IIxFcicWvAU+LPPDPGH/YYYfjmM1qwA/8R6SU9G0RyXA6KKVUG0Ym1Qx0AAGl1FPORnUJJUqpDjBeBECxw/FE478Bv3Y6CBMRuRNoU0oddzqWKKwHbhCRgyLynIi4ZWr2nwD/W0RaMF4TTn0yA0BEqoGrgINY/BrwrMiLyB1At1LqsNOxRCER2AV8Qyl1FTCMM2WHNxGp7d0FrALKgAwR+aCzUXkLEfkrjI/Z/+l0LAAikg78FUbJwY0kAnkY5Yg/Ax4WEXE2JMD4hPFZpVQF8Fkin7qdQEQygUeBP1FKDVp9fc+KPHA9cKeINAE/BvaJyA+dDWmaVqBVKXUw8vNPMETfaW4Fziul/EqpSeCnwHUOxzSbLhEpBYgc4/7xfi5E5D7gDuADyj0bTNZgvGkfj7wWyoEjIrLC0agu0gr8VBkcwvjU7cjC8Czuw/j7B3gEiPvCK4CIJGEI/H8qpcx4LH0NeFbklVJ/qZQqV0pVYywePqOUckVWqpTqBFpEZEPkoVuA1x0MyaQZuEZE0iPZ1C24YEF4Fr/AeAESOf7cwVimEZH9wF8AdyqlRpyOx0QpdUIpVayUqo68FlqBXZG/QTfwM2AfgIisB5JxR/fHduCmyPf7gIZ4BxB5DX4HOK2U+scZT1n7GlBKef4LuBl43Ok4ZsW0E6gF6jD+0POcjikS1xeBM8BJ4AdAioOxPISxNjCJIU4fBQowHAUNkWO+S+JqBFqAY5Gvb7rlv9ms55uAQrfEhiHqP4z8vR0B9rkkrrcAh4HjGHXw3Q7E9RaMBeC6GX9Xb7f6NaDbGmg0Gs0SxrPlGo1Go9FcGS3yGo1Gs4TRIq/RaDRLGC3yGo1Gs4TRIq/RaDRLGC3yGo1Gs4TRIq/RaDRLmP8fY28y1KHfQvAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YTGpyY7Dmi2y"
      },
      "source": [
        "# Modeling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tIog3Y4Qmi2y"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import activations"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1tiELmKJmi2y",
        "outputId": "ecc8b6af-43e6-4efb-c69c-3e4909075915"
      },
      "source": [
        "# [samples, time steps, features]\n",
        "ds = tf.keras.preprocessing.timeseries_dataset_from_array(data=X_train.drop(['x'], axis=1), # x was droped\n",
        "                                                     targets=y_train,\n",
        "                                                    sequence_length=1)\n",
        "x, y = next(iter(ds))\n",
        "x.shape[1:]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([1, 5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cim3JFqQmi2z",
        "outputId": "d76ac459-331d-472a-cb43-0201d6881789"
      },
      "source": [
        "model = tf.keras.models.Sequential()\n",
        "model.add(layers.LSTM(700, input_shape=x.shape[1:], activation='tanh', return_sequences=True))\n",
        "model.add(layers.Dropout(0.2))\n",
        "model.add(layers.Dense(1, activation='linear'))\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm (LSTM)                  (None, 1, 700)            1976800   \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 1, 700)            0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1, 1)              701       \n",
            "=================================================================\n",
            "Total params: 1,977,501\n",
            "Trainable params: 1,977,501\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tjN3jXSRmi2z"
      },
      "source": [
        "model.compile(loss='mean_squared_error', optimizer=\"adam\", metrics=\"mean_absolute_error\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "262_hfAZmi2z"
      },
      "source": [
        "l_history = model.fit(x, y, epochs=7000)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qd3Mtaanmi20"
      },
      "source": [
        "# Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z-qFg4Tumi20",
        "outputId": "c07fc45b-b9f6-4b77-fda3-286116bb1a84"
      },
      "source": [
        "y_pred = model.predict(X_test.drop('x', axis=1).to_numpy().reshape(X_test.shape[0],1,X_test.shape[1]-1)).reshape(X_test.shape[0])\n",
        "y_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-12.671094  , -13.964843  , -13.579772  , -10.968188  ,\n",
              "        -6.415969  ,  -0.35846546,   5.904522  ,  11.2642765 ,\n",
              "        15.021201  ,  15.9130945 ,  14.894861  ,  11.553034  ,\n",
              "         6.285661  ,  -0.64560974,  -7.623312  , -12.686525  ,\n",
              "       -15.107612  , -15.826831  , -14.850017  , -11.664974  ,\n",
              "        -5.781737  ,   1.8105255 ,   9.119691  ,  14.743664  ,\n",
              "        17.751518  ,  17.975653  ,  16.027935  ], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bxXAYhu_mi20",
        "outputId": "33a0f473-800f-47b7-ee51-5bb13ac2f7b3"
      },
      "source": [
        "plt.plot(X_train.x, y_train)\n",
        "plt.plot(X_test.x, y_pred)\n",
        "plt.plot(X_test.x, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x2348d665730>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MjLO2Hgjmi21"
      },
      "source": [
        "# Validate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XhsYcjvjmi21"
      },
      "source": [
        "# x_validate = np.arange(3.1, 30, 0.2)\n",
        "# # Our function\n",
        "# y_validate = (np.sin(2*x_validate) * x_validate) + np.random.rand(x_validate.size) * 0.2\n",
        "# val_dataset = pd.DataFrame.from_dict(dict(zip(x_validate.tolist(), y_validate.tolist())), orient='index').reset_index()\n",
        "# val_dataset.columns = ['x', 'y']\n",
        "# val_dataset = val_dataset[[\"y\", \"x\"]]\n",
        "# test_val = val_dataset\n",
        "# for i in range(1, 6):\n",
        "#     test_val = pd.concat([test_val, create_shifted_df(val_dataset, i)], axis=1)\n",
        "# test_val = test_val.dropna()\n",
        "# val_df = test_val.tail(40)\n",
        "# val_df.head(3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3wdEfJ1jmi21"
      },
      "source": [
        "# y_val_pred = model.predict(val_df.drop(['y','x'], axis=1).to_numpy().reshape(val_df.shape[0],1,val_df.shape[1]-2)).reshape(val_df.shape[0])\n",
        "# y_val_pred"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5qse5elgmi21"
      },
      "source": [
        "# plt.plot(test_val.x, test_val.y)\n",
        "# plt.plot(val_df.x, y_val_pred)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}